Personalized food item design and culinary fulfillment system

ABSTRACT

A system and method for personalized food item designer and culinary fulfillment. The system is a cloud-based network containing a food item design server, portals for restaurants and patrons, to enter their information, and a recipe generator which creates a unique dietary experience for patrons based on a multitude of variables associated with the business enterprises, patrons historic culinary transactions, dietary needs and preferences both explicit and inferred. The system may be accessed through web browsers or purpose-built computer and mobile phone applications.

CROSS-REFERENCE TO RELATED APPLICATIONS

Application Ser. No. Date Filed Title Current Herewith PERSONALIZED FOODapplication ITEM DESIGN AND CULINARY FULFILLMENT SYSTEM Claims benefitof, and priority to: 62/984,237 Mar. 2, 2020 PERSONALIZED FOOD ITEMDESIGN AND CULINARY FULFILLMENT SYSTEM and is also acontinuation-in-part of: 16/993,488 Aug. 14, 2020 FOOD ORDER MANAGEMENTSYSTEM AND METHOD THEREOF which claims benefit of, and priority to:62/956,289 Jan. 1, 2020 FOOD ORDER MANAGEMENT SYSTEM AND METHOD THEREOFthe entire specification of each of which is incorporated herein byreference.

BACKGROUND Field of the Art

The disclosure relates to the field of computerized comparative andartificial intelligent systems, and more particularly to the field ofcomputerized systems for food item personalization, optimization,business selection, food ordering, for retail business establishmentsand its patrons.

Discussion of the State of the Art

People frequently wishing to dine at a retail business establishment arelimited to ordering and consuming a limited set of food items based on arestaurants long standing menu with limited manual customization thattake into account a patron's dietary preferences or desired long-termoutcomes. Similarly, restaurants are not free to dynamically change menuitems based on ingredients on hand and/or culinary skills available thatmaximizes their business outcomes and impact on a particular patronand/or prospective patron dining experience. The result is often asuboptimal dining experience for restaurant consumers and reduced longterm viability for the restaurant.

There is currently no automated system that personalizes and optimizesfood item recipe generation and fulfilment to address theseshortcomings.

What is needed is a system and method for automated personalized fooditem design and culinary fulfilment to optimize the dining experiencefor both the patron and the dining establishment.

SUMMARY

Accordingly, the inventor has conceived, and reduced to practice, asystem and method for automated personalized food item design andculinary fulfillment. The system is a cloud-based network containing afood item design engine, portals for restaurants and patrons, to entertheir information, and a recipe generator which creates a unique dietaryexperience for patrons based on a multitude of variables associated withthe business enterprises, patrons historic culinary transactions,dietary needs and preferences both explicit and inferred. The system maybe accessed through web browsers or purpose-built computer and mobilephone applications.

According to a preferred aspect, a system for automated personalizedfood item design and culinary fulfillment, comprising: a businessenterprise database comprising a plurality of business enterpriselocations, business enterprise information comprising a food itemprovided by a business enterprise location, and a plurality ofingredients for each food item; and a food item design engine comprisinga first plurality of programming instructions stored in a memory of, andoperable on a processor of, a computing device, wherein the firstplurality of programming instructions, when operating on the processor,cause the computing device to: receive consumer food item preferenceinformation from a plurality of consumer computing devices for one ormore food item requests, the food item information for each food itemrequest comprising desired food type, food amount and timeframedescriptors; retrieve the consumer profile from the consumer profiledatabase; retrieve the consumer culinary transactions from the culinarytransaction database; receive recipe information from one or morebusiness enterprise devices each associated with a business, the recipeavailability information comprising business location, skill set ofchefs, availability of ingredients; analyze the ingredients and comparewith culinary transactions, consumer profile, recipe data and consumerfood item preference request information; generate a consumer-specificfood item based on real-time data from consumer food item preference,historical data from consumer profile, culinary transaction, recipedata, ingredient availability, preparation options, skill set of on-dutychefs; send a food item recommendation to consumers compute device, therecommendation comprising a food item description, name and location ofbusiness and estimate of availability for pick up or in-establishmentdining, send a food item order request to the business enterprisecomputing device and specification for pick up or in-establishmentdining.

According to another preferred aspect, a method for automatedpersonalized food item design and culinary fulfillment, comprising: abusiness enterprise database comprising a plurality of businessenterprise locations, business enterprise information comprising a fooditem provided by a business enterprise location, and a plurality ofingredients for each food item; and a food item design engine comprisinga first plurality of programming instructions stored in a memory of, andoperable on a processor of, a computing device, wherein the firstplurality of programming instructions, when operating on the processor,cause the computing device to: receive consumer food item preferenceinformation from a plurality of consumer computing devices for one ormore food item requests, the food item information for each food itemrequest comprising desired food type, food amount and timeframedescriptors; retrieve the consumer profile from the consumer profiledatabase; retrieve the consumer culinary transactions from the culinarytransaction database; receive recipe information from one or morebusiness enterprise devices each associated with a business, the recipeavailability information comprising business location, skill set ofchefs, availability of ingredients; analyze the ingredients and comparewith culinary transactions, consumer profile, recipe data and consumerfood item preference request information; generate a consumer-specificfood item based on real-time data from consumer food item preference,historical data from consumer profile, culinary transaction, recipedata, ingredient availability, preparation options, skill set of on-dutychefs; send a food item recommendation to consumers compute device, therecommendation comprising a food item description, name and location ofbusiness and estimate of availability for pick up or in-establishmentdining, send a food item order request to the business enterprisecomputing device and specification for pick up or in-establishmentdining.

According to an aspect of an embodiment, the food item design engineoptimization is based on artificial intelligence to create a unique fooditem for a particular patron.

According to an aspect of an embodiment, the food item design engineoptimization is based on artificial intelligence to create a unique fooditem for a typical patron.

According to an aspect of an embodiment, the business enterpriseinformation further comprises hours of availability of culinary chefswith required food preparation skills.

According to an aspect of an embodiment, the patron provided real-timefood item information is vague and therefore inferred by system.

According to an aspect of an embodiment, the patron provided real-timefood item information is updated with feedback information rating theirexperience.

According to an aspect of an embodiment, the external data resourcesincludes rating data from social media sites.

According to an aspect of an embodiment, the external data resourcesincludes rating data from restaurant review sites.

According to an aspect of an embodiment, the external data resourcesincludes health care service provider data.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawings illustrate several aspects and, together withthe description, serve to explain the principles of the inventionaccording to the aspects. It will be appreciated by one skilled in theart that the particular arrangements illustrated in the drawings aremerely exemplary, and are not to be considered as limiting of the scopeof the invention or the claims herein in any way.

FIG. 1 is a block diagram illustrating an exemplary system architecturefor an automated personalized food item design and culinary fulfillmentsystem.

FIG. 2 is a block diagram illustrating an exemplary architecture for anaspect of an automated food item design engine.

FIG. 3 is a block diagram illustrating an exemplary architecture for anaspect of an automated culinary fulfilment engine.

FIG. 4 is a flow diagram showing the steps of an exemplary method forpersonalized food item design, selection, restaurant selection, orderfulfilment and receipt by a restaurant patron.

FIG. 5 is a flow diagram showing the steps of an exemplary method for anoptimized food item recipe generation process based on a particularpatron current food preferences, historical culinary transactions,current geographic location, and the restaurant's ingredients on handand culinary skills.

FIG. 6 is a flow diagram showing the steps of an exemplary method for anoptimized food item recipe generation process based on the restaurants'food ingredients on hand, culinary skills and a predicted patronpreference.

FIG. 7 is a block diagram illustrating an exemplary hardwarearchitecture of a computing device.

FIG. 8 is a block diagram illustrating an exemplary logical architecturefor a client device.

FIG. 9 is a block diagram showing an exemplary architectural arrangementof clients, servers, and external services.

FIG. 10 is block diagram illustrating another aspect of an exemplaryhardware architecture of a computing device.

FIG. 11 is a message diagram showing exemplary messaging between patrondevice and recipe generation system with output to the recipeoptimization system.

FIG. 12 is a message diagram showing exemplary messaging within therecipe optimization system taking inputs from a recipe generation systemand a recipe validation system and providing an optimized personalizedrecipe information as an output to restaurant recommendation system.

FIG. 13 is a message diagram showing exemplary messaging within arestaurant recommendation system with various inputs and providingculinary preparation and personalized food item output information.

DETAILED DESCRIPTION

The inventor has conceived, and reduced to practice, a system and methodfor personalized food item designer and culinary fulfillment. The systemis a cloud-based network containing a food item design server, portalsfor restaurants and patrons, to enter their information, and a recipegenerator which creates a unique dietary experience for patrons based ona multitude of variables associated with the business enterprises,patrons historic culinary transactions, dietary needs and preferencesboth explicit and inferred. The system may be accessed through webbrowsers or purpose-built computer and mobile phone applications.

It is frequently the case that a person wishes to order food from arestaurant that meets a set of explicit requirements (e.g. healthy,fast, good price value, etc.) as well as an implicit requirement(reputable establishment, high quality ingredients, etc.). Additionally,the person has longer term nutritional goals (e.g. maintain healthyweight, blood pressure, energy level, etc.) that should be taken inconsideration as they decide where and what to dine on. However,achieving the desired outcome using currently available tools isdifficult and in doing so often results in a suboptimal experience forthe patron and loss of viability for the business. The person couldselect a restaurant by chance, usually by seeing a sign for a restaurantwhile driving. Alternately, they could try to find a restaurant bysearching using a mobile device. In this case, the person first has toopen an application, search for nearby restaurants, and select arestaurant by clicking on it. However, in doing so, the decision is,again, based largely on chance, as the driver is forced to make arestaurant selection from restaurants shown in the nearby area and basedonly on the restaurant name, which may or may not indicate a type ofcuisine (e.g., Italian food, American food, Mexican food, Japanese food,etc.). If the person wishes to get additional information, such as menuoptions, pricing, etc., the person is forced to take additional stepsand time to researching restaurant websites, opening up menus, orcalling the restaurant for more information. All of these methods areinefficient and none of them takes into account a myriad of factors thatmay affect the decision such as the person's current food preferences,historical culinary transactions, restaurants ingredients on hand andculinary skills available.

The invention is particularly useful to both restaurants and theirpatrons in personalizing and optimizing the dining experience.Personalized food item design enables restaurants to differentiatethemselves by offering a unique menu that caters to their patron's needswhile optimizing the food ingredients and culinary skills on hand.Patrons can select food items based on their current and past dietaryrequirements and preferences. As will be further disclosed herein, theinvention makes a multivariate analysis of a large variety of factors(patron preferences; restaurant location, ingredient on-hand, culinaryskill; social validation; etc.) to allow a patron to gain access topersonalized food items fulfilled by convenient restaurant selectionwhich optimize their dining experience and longer term dietary goals.

While the use case of patrons searching for food at a diningestablishment is a primary example used herein, it is important to notethat the invention is not so limited, and may be used by any person(e.g., person preparing food from home) seeking to purchase food itemsor ingredients at any retail business establishment (i.e., the inventionis not limited to restaurants, and can be applied to any retail goods,such as grocery stores, on-line and/or brick and mortar; home foodinventory).

One or more different aspects may be described in the presentapplication. Further, for one or more of the aspects described herein,numerous alternative arrangements may be described; it should beappreciated that these are presented for illustrative purposes only andare not limiting of the aspects contained herein or the claims presentedherein in any way. One or more of the arrangements may be widelyapplicable to numerous aspects, as may be readily apparent from thedisclosure. In general, arrangements are described in sufficient detailto enable those skilled in the art to practice one or more of theaspects, and it should be appreciated that other arrangements may beutilized and that structural, logical, software, electrical and otherchanges may be made without departing from the scope of the particularaspects. Particular features of one or more of the aspects describedherein may be described with reference to one or more particular aspectsor figures that form a part of the present disclosure, and in which areshown, by way of illustration, specific arrangements of one or more ofthe aspects. It should be appreciated, however, that such features arenot limited to usage in the one or more particular aspects or figureswith reference to which they are described. The present disclosure isneither a literal description of all arrangements of one or more of theaspects nor a listing of features of one or more of the aspects thatmust be present in all arrangements.

Headings of sections provided in this patent application and the titleof this patent application are for convenience only, and are not to betaken as limiting the disclosure in any way.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or morecommunication means or intermediaries, logical or physical.

A description of an aspect with several components in communication witheach other does not imply that all such components are required. To thecontrary, a variety of optional components may be described toillustrate a wide variety of possible aspects and in order to more fullyillustrate one or more aspects. Similarly, although process steps,method steps, algorithms or the like may be described in a sequentialorder, such processes, methods and algorithms may generally beconfigured to work in alternate orders, unless specifically stated tothe contrary. In other words, any sequence or order of steps that may bedescribed in this patent application does not, in and of itself,indicate a requirement that the steps be performed in that order. Thesteps of described processes may be performed in any order practical.Further, some steps may be performed simultaneously despite beingdescribed or implied as occurring non-simultaneously (e.g., because onestep is described after the other step). Moreover, the illustration of aprocess by its depiction in a drawing does not imply that theillustrated process is exclusive of other variations and modificationsthereto, does not imply that the illustrated process or any of its stepsare necessary to one or more of the aspects, and does not imply that theillustrated process is preferred. Also, steps are generally describedonce per aspect, but this does not mean they must occur once, or thatthey may only occur once each time a process, method, or algorithm iscarried out or executed. Some steps may be omitted in some aspects orsome occurrences, or some steps may be executed more than once in agiven aspect or occurrence.

When a single device or article is described herein, it will be readilyapparent that more than one device or article may be used in place of asingle device or article. Similarly, where more than one device orarticle is described herein, it will be readily apparent that a singledevice or article may be used in place of the more than one device orarticle.

The functionality or the features of a device may be alternativelyembodied by one or more other devices that are not explicitly describedas having such functionality or features. Thus, other aspects need notinclude the device itself.

Techniques and mechanisms described or referenced herein will sometimesbe described in singular form for clarity. However, it should beappreciated that particular aspects may include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. Process descriptions or blocks in figures should beunderstood as representing modules, segments, or portions of code whichinclude one or more executable instructions for implementing specificlogical functions or steps in the process. Alternate implementations areincluded within the scope of various aspects in which, for example,functions may be executed out of order from that shown or discussed,including substantially concurrently or in reverse order, depending onthe functionality involved, as would be understood by those havingordinary skill in the art.

Definitions

“Business establishment” or “place of business” as used herein mean thelocation of any business entity with which customers may transactbusiness. Typically, this will be a physical location where customersmay enter the location and transact business directly with employees ofthe business, but may also be a delivery-based business. Many examplesherein use a restaurant as the business establishment, but the inventionis not limited to use in restaurants, and is applicable to any businessestablishment. “Patron” is used to reference the customer or prospectivecustomer of the business establishment.

Conceptual Architecture

FIG. 1 is a block diagram illustrating an exemplary system architecture100 for a personalized food item design and culinary fulfilment system,according to a preferred aspect. According to an aspect, and using arestaurant as an exemplary business establishment, system 100 comprisesa food item design engine 200, a patron portal 120, a restaurant portal140, databases 150, and a culinary fulfillment engine 300. Patron mobiledevices 121 may connect to patron portal 120, typically via a cellularphone network 160, although connections may be made through other means,as well, such as through Internet 170 (e.g., through a Wi-Fi router).Restaurant computers 141 and/or restaurant mobile devices 131 mayconnect to restaurant portal 140, typically through an Internet 170connection, although other network connections may be used.

According to an aspect, a patron may be enroute to a destination, suchas her home. The patron may connect to patron portal 120 to pre-enter avariety of preferences and other information that may be stored in adatabase 150, and used by food item design engine 200 to suggestpersonalized food items that meet the patron's preferences. Examples ofthe types of preferences that a patron may enter include, but are notlimited to: food preferences such as types of food (e.g. ethnicity suchas Chinese, American, Greek, as well as for example style such as spicyor soup and salad or steakhouse fare, etc.), frequency with whichpreferred foods are eaten, ranking of particular foods relative to otherfoods, patrons inconvenience preferences such as time delays anddistance/time required of detour, food attributes such as price,calories, ingredients, and side dishes. In some aspects, certain ofthese preferences may be determined by system 100. For example, thetypes of food preferred by the patron and the frequency with whichpreferred foods are eaten may be determined based on the culinarytransaction history of usage or stored in a database 150 in the system.Other such preferences and factors may also be determined by systemthrough access to one or more external resources 180 such as a healthservice provider that may include known food allergies, blood pressurehistory, diabetic information and so forth. Other exemplary externalresources may comprise research organizations such as National Libraryof Medicine, government data sources such as data.gov, corporate sourcessuch as Registry of Open Data (RODA) on Amazon Web Services

Likewise, restaurants may connect to restaurant portal 140 to enterinformation about the restaurant and its menu. Examples of the types ofinformation that a restaurant may enter include, but are not limited to:restaurant name, location, types of food offered, hours of operation,phone number, specific menu offerings, food preparation times forcertain dishes (including adjustments to food preparation times duringbusy periods for the restaurant), prices, calorie counts, ingredients,side dishes, drinks, and special pricing options like daily “happy hour”specials or seasonal offerings. In some aspects, the system may be ableto determine certain restaurant information by accessing externalresources 180 such as mapping websites and applications. For example,system may access a publicly-available mapping website such as Googlemaps, which may contain information about the restaurant's name,location, types of food offered, hours of operation, phone number, etc.Thus, in some aspects, it is not necessary for the restaurant to entercertain information through portal, as the information may beautomatically obtained from external resources 180.

When a patron mobile device 121 connects to personalized food itemdesign and culinary fulfilment system 110 and the patron requestsen-route food item assistance, food item design engine 200 retrieves thepatron preferences from a database 150. The patron may further enteradditional food item preferences and a destination or select apre-entered destination presented from the patron's preferences throughpatron real-time update engine 211, which will allow the system tobetter customize its restaurant suggestions. A culinary fulfilmentengine 300 then determines the patron's location by querying thepatron's mobile device for location information (e.g., provided by themobile device's GPS hardware, Wi-Fi location applications, etc.) andgathers information from external resources 180 about restaurant optionslocated nearby and along the route from the patron's currently locationto the patron's destination, as well as traffic information related tothe patron's location, intended route, and identified restaurantoptions. A culinary fulfilment engine 300 retrieves additionalinformation from a database about identified restaurant options, if suchinformation is available. Based on the patron preferences, restaurantinformation, and traffic information, culinary fulfilment engine 300identifies one or more restaurants and one or more food optionsavailable at those restaurants that are compatible with the patron'spreferences, and presents the identified restaurants and theircorresponding food options to the patron on the patron's mobile device121 as suggestions along with indications of the additional delay thatwill be caused by choosing each suggestion.

In some aspects, an application on patron's mobile device 121 may dialthe phone number of the chosen restaurant for the patron to place theorder via voice and combination of text message. In an aspect, culinaryfulfilment server 300 will contact the restaurant through restaurantportal 140 to automatically enter an order into the restaurant'scomputer 141, or to direct an employee of the restaurant to call thepatron's mobile device 121, or to establish a voice connection betweenthe restaurant and the patron's mobile device 121 through another means(e.g., voice over internet protocol, or VOIP).

In some aspects, culinary fulfilment engine 300, through restaurantportal 140, may also provide information to the restaurant to schedulethe restaurant's food preparation activities to coordinate with thepatron's arrival. If the restaurant has entered information such as foodpreparation times, culinary fulfilment engine 300 may use thatinformation to instruct the restaurant's kitchen staff when to startpreparation of the patron's order, such that the order will be readyjust prior to arrival of the patron. Such food preparation times andscheduling may be adjusted for busy periods at the restaurant (typicallyaround lunch and dinner) either automatically based on the restaurant'shistory as stored in a database 150, or by retrieving information storedin a database 150 that has been manually entered by the restaurantthrough restaurant portal 140.

FIG. 2 is a block diagram illustrating an exemplary architecture for anaspect of an automated food item design engine 200. According to anaspect, a food item design engine 200 comprises several subsystems, arecipe generation subsystem 210, a recipe optimization subsystem 220,and aa recipe validation subsystem 230. A recipe generation subsystemcomprises a patron real-time update engine 211, a patron profile 212,patron culinary transaction 213, recipe generator engine 214, restaurantingredient data 215, and restaurant recipe data 216. A patron real-timeupdate engine 211 enables the patron to provide up-to-date food iteminput by the patron using an application on his or her mobile device131. A patron profile 212, patron culinary transactions 213, restaurantingredient data 215, and restaurant recipe data 216 may be retrievedfrom a database 150 or, in some aspects, obtained from externalresources 180.

A recipe optimization subsystem comprises a recipe optimizer 222, ahealth data retriever 221, and a cost data retriever 223. A health dataretriever 221 obtains health data from external sources 180, that mayinclude a health provider system, while a cost data retriever 223 mayeither obtain cost data from a database 150 or from external resources180.

A recipe validation subsystem 230 comprises patron review data 231, avalidation engine 232, and wearable data 233. A validation engine maytake as input patron review data, wearable data and personal food iteminformation; and provides as output updates to a food item recipe to arecipe optimizer 222.

In operation, when a patron is desiring food item assistance a recipegenerator engine 214 receives the patron's current food itemrequirements from a patron real time update engine 211 along with apatron profile 213. A recipe generation engine 214 obtains restaurantingredient data 215 and restaurant recipe data 216 for one or morerestaurants either from a database 150 or from external resources 180. Arecipe generation engine 214 then uses machine learning algorithms tocreate a personalized food item optimized to meet the patron preferencesand outcomes.

A recipe generator engine 214 presents recommendations to the patronabout food items meeting the patron's preferences and allows the patronto select an option on his or her mobile device 121 by simply selectingan option (on a touch-based mobile device interface, for example). Arecipe generator engine 214 then sends the information about theselected recipe to a recipe optimizer 222, which obtains health datafrom health data retriever 221 and cost from cost data retriever 223 andoptimizes the recipe. Optimization may occur around one or moreparameters including health, cost, restaurant dining experience, etc.depending on patrons near and long-range goals and stated outcomes. Oncecomplete, recipe optimizer engine 222 sends personalized recipeinformation 241 to a culinary fulfillment engine 300.

In some aspects, food item design 200 engine may have a recipevalidation subsystem 230, in which a validation engine 232 receivesfeedback from the patron's experience from patron review data 231 andpatrons wearable information 233 and associates with a personalized fooditem 242. The feedback in the form of subjective text comments and/orobjective measurements (e.g. blood pressure, glucose levels) may then beupdated in the patron's culinary transactions 213 for use in future fooditem optimization.

Note that this example is simplified for clarity, and that food itemdesign engine 200 will address a much broader set of factors andvariables, as described elsewhere herein. The food item design enginemay use any number of optimization algorithms, including machinelearning algorithms or others known in the art, to find optimalsolutions to the large number of variables presented.

FIG. 3 is a block diagram illustrating an exemplary architecture for anaspect of an automated culinary fulfilment engine. According to anaspect, culinary fulfilment engine 300 comprises, a restaurantrecommendation system 310, comprising a personalized recipe information241, patron location data 312, traffic data 313, a recommendation engine314, restaurant location data 315, restaurant skill data 316, restaurantreview data 317, culinary preparation information 318, and patronpersonalized food item 242.

In operation, recommendation engine 314 will take as inputs apersonalized recipe information 241, patron location data 312, trafficdata 313, restaurant location data 315, restaurant skill data 316,restaurant review data 317. Using semantic vector space methods familiarto those skilled in the art, the input data is represented as wordvector and compared using cosine similarity techniques with theoptimized target vector to provide as outputs a culinary preparationinformation 318 that is used by the restaurant and a patron personalizedfood item 242 that is displayed to the patron.

Detailed Description of Exemplary Aspects

FIG. 4 is a flow diagram showing the steps of an exemplary method forpersonalized food item design, selection, restaurant selection, orderfulfilment by selected restaurant. A patron portal is provided for thepatron to pre-enter preferences such as food types, food attributes,diet restrictions, health goals, and other preferences 401 thisinformation is subsequently stored in a historical database 403 forfuture use. During mealtime and/or when patron is mobile, the patron ispresented with an interface on mobile app to make real-time preferenceson meal interests or desires for food ingestion, the app may ask “fordining, what are you in the mood for?” 402. An analysis (as furtherexemplified in FIG. 5) is performed on patrons historical and real-timefood item requirements and compared to menu options and culinarycapabilities of restaurants in proximity of patron 404 from which aconsumer specific food item is generated 405. The food item options 406are displayed to the patron, along with a recommended restaurant, withdetails such as type of food, food cost, additional drive time 407. Achoice is made from the patron 408 for one or more food item displayedwith its recommended restaurant. The patron's food item information issent to the restaurant, confirmation to patron and food item fulfilment409. Display food item confirmation along with restaurant detailsincluding restaurant address, driving, estimated travel time andestimated food item availability 410. Notify and update patron on orderstatus and restaurant fulfilment 411.

FIG. 5 is a flow diagram showing the steps of an exemplary method for anoptimized food item recommendation to a particular restaurant patronbased upon their preferences and patron profile. Convert patron fooditem text documents to corresponding word vector 501. Convert restaurantrecipe, restaurant ingredient data and culinary preparation skill textdocuments to corresponding word vectors 502. Using a matrix dimensionreduction technique such as principal dimension analysis or others knownto those skilled in the art, reduce the input matrix for more effectiveprocessing. Compare resultant vectors using semantic term vector spacetechniques known to one in the art 503. Select restaurant word vectorthat is most similar to the patron food item requirement 504. Modifyrestaurant recipe items based on restaurant ingredients, culinarycapabilities to most closely align to patron's requirements 505. Outputfood item description and recipe to patron and restaurant 506.

FIG. 6 is a flow diagram showing the steps of an exemplary method for anoptimized food item based on the restaurants' food ingredients on hand,culinary skills and a predicted preference of a patron. Convertaggregate historical patron food item text documents to correspondingword vectors to represent generalized patron food profile 601. Convertrestaurant recipe and culinary preparation text documents tocorresponding word vectors 602. Compare resultant vectors using termvector space techniques 603. Select restaurant word vector that is mostsimilar to the generalized patron food item requirement 604. Modifyrestaurant recipe items based on restaurant ingredients, culinarycapabilities to most closely align to generic patron's requirements 605.Output food item menu to patron 606.

An exemplary semantic comparison method may include term vector spaceanalysis technique to those familiar in the art. Term vector modeling isan algebraic model for representing text and text documents as vectors.Each term or word in a text document typically corresponds to adimension in that vector. Once a text document is described as a wordvector, comparisons between two vectors may be made using vectorcalculus. One useful technique to determine similarities betweendocuments is by comparing the deviation of angles between each documentvector and the original query vector where the query is represented as avector with same dimension as the vectors that represent the otherdocuments.

An exemplary dimensional reduction technique familiar to those skilledin the art is Principal Component Analysis (“PCA”), which may be used tooptimize the variables prior to vectorization to reduce dimensionalityof resulting vectors prior to feeding into a machine learning algorithm.

An exemplary recipe optimization method may include deep learningtechniques familiar to those skilled in the art. One such form of deeplearning that is particularly useful when generating text is RecurrentNeural Networks (“RNN”) using long short-term memory (“LSTMs”) units orcells. A single LSTM is comprised of a memory-containing cell, an inputgate, an output gate and a forget gate. The input and forget gatedetermine how much of incoming values transit to the output gate and theactivation function of the gates is usually a logistic function. Theinitial input data will cause the model to learn the weights ofconnections that influence the activity of these gates which will impactthe resultant output. To generate unique personalized recipes for agiven patron, standard recipes along with the patron profile data arefed into the input gate of the RNN, in turn the RNN will learn what'simportant to the patron and create unique recipe outputs.

FIG. 11 is a message diagram showing exemplary messaging between patrondevice 110 and recipe generation system with output to the recipeoptimizer 222. Initially, a patron device 110 connects to a patronportal 120 to submit a food item request. The request may then berelayed by the patron portal 120 to a patron realtime update engine 211,which then relays the request to a recipe generator engine 214 andupdates the patron's profile in a database 150. Recipe generator engine214 acknowledges the request and retrieves stored patron profile andprevious culinary transactions from the database 150, and uses thisinformation to generate personalized recipe data for the specific patronthat is then sent to the recipe optimizer 222.

FIG. 12 is a message diagram showing exemplary messaging within therecipe optimization system taking inputs from a recipe generation systemand a recipe validation system and providing an optimized personalizedrecipe information as an output to restaurant recommendation system.Patron review data 231, submitted by patrons, and patron wearable data233, transmitted by wearable devices patrons may be wearing, arereceived at a validation engine 232. Validation engine 232 uses thisinformation to produce a validated recipe rating that is sent to arecipe optimizer 222, which then retrieves patron health data and costdata associated with the recipe (for example, ingredient costs and preptimes) using health data retriever 221 and cost data retriever 223,respectively. This information is used to further adjust the recipe andproduce personalized recipe information 241, which is then sent asoutput to a recommendation engine 314.

FIG. 13 is a message diagram showing exemplary messaging within arestaurant recommendation system with various inputs and providingculinary preparation and personalized food item output information.Personalized recipe information 241 is received at a recommendationengine 314 from a recipe optimizer 222, as described above (withreference to FIG. 12). Recommendation engine 314 also receivesinformation from a number of sources to assist with producing a specificrecipe recommendation, including (but not limited to) patron locationdata 312, traffic data 313, restaurant location data 315, restaurantskill data 316 (such as the skills of individual chefs that are workingat the time), and restaurant review data 317. This aggregatedinformation may then be used to produce a patron-specific personalizedfood item 242, along with a set of culinary instructions for preparingthe patron-specific item that may be sent as culinary preparationinformation 318.

Hardware Architecture

Generally, the techniques disclosed herein may be implemented onhardware or a combination of software and hardware. For example, theymay be implemented in an operating system kernel, in a separate userprocess, in a library package bound into network applications, on aspecially constructed machine, on an application-specific integratedcircuit (ASIC), or on a network interface card.

Software/hardware hybrid implementations of at least some of the aspectsdisclosed herein may be implemented on a programmable network-residentmachine (which should be understood to include intermittently connectednetwork-aware machines) selectively activated or reconfigured by acomputer program stored in memory. Such network devices may havemultiple network interfaces that may be configured or designed toutilize different types of network communication protocols. A generalarchitecture for some of these machines may be described herein in orderto illustrate one or more exemplary means by which a given unit offunctionality may be implemented. According to specific aspects, atleast some of the features or functionalities of the various aspectsdisclosed herein may be implemented on one or more general-purposecomputers associated with one or more networks, such as for example anend-user computer system, a client computer, a network server or otherserver system, a mobile computing device (e.g., tablet computing device,mobile phone, smartphone, laptop, or other appropriate computingdevice), a consumer electronic device, a music player, or any othersuitable electronic device, router, switch, or other suitable device, orany combination thereof. In at least some aspects, at least some of thefeatures or functionalities of the various aspects disclosed herein maybe implemented in one or more virtualized computing environments (e.g.,network computing clouds, virtual machines hosted on one or morephysical computing machines, or other appropriate virtual environments).

Referring now to FIG. 7, there is shown a block diagram depicting anexemplary computing device 10 suitable for implementing at least aportion of the features or functionalities disclosed herein. Computingdevice 10 may be, for example, any one of the computing machines listedin the previous paragraph, or indeed any other electronic device capableof executing software- or hardware-based instructions according to oneor more programs stored in memory. Computing device 10 may be configuredto communicate with a plurality of other computing devices, such asclients or servers, over communications networks such as a wide areanetwork a metropolitan area network, a local area network, a wirelessnetwork, the Internet, or any other network, using known protocols forsuch communication, whether wireless or wired.

In one aspect, computing device 10 includes one or more centralprocessing units (CPU) 12, one or more interfaces 15, and one or morebusses 14 (such as a peripheral component interconnect (PCI) bus). Whenacting under the control of appropriate software or firmware, CPU 12 maybe responsible for implementing specific functions associated with thefunctions of a specifically configured computing device or machine. Forexample, in at least one aspect, a computing device 10 may be configuredor designed to function as a server system utilizing CPU 12, localmemory 11 and/or remote memory 16, and interface(s) 15. In at least oneaspect, CPU 12 may be caused to perform one or more of the differenttypes of functions and/or operations under the control of softwaremodules or components, which for example, may include an operatingsystem and any appropriate applications software, drivers, and the like.

CPU 12 may include one or more processors 13 such as, for example, aprocessor from one of the Intel, ARM, Qualcomm, and AMD families ofmicroprocessors. In some aspects, processors 13 may include speciallydesigned hardware such as application-specific integrated circuits(ASICs), electrically erasable programmable read-only memories(EEPROMs), field-programmable gate arrays (FPGAs), and so forth, forcontrolling operations of computing device 10. In a particular aspect, alocal memory 11 (such as non-volatile random access memory (RAM) and/orread-only memory (ROM), including for example one or more levels ofcached memory) may also form part of CPU 12. However, there are manydifferent ways in which memory may be coupled to system 10. Memory 11may be used for a variety of purposes such as, for example, cachingand/or storing data, programming instructions, and the like. It shouldbe further appreciated that CPU 12 may be one of a variety ofsystem-on-a-chip (SOC) type hardware that may include additionalhardware such as memory or graphics processing chips, such as a QUALCOMMSNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly commonin the art, such as for use in mobile devices or integrated devices.

As used herein, the term “processor” is not limited merely to thoseintegrated circuits referred to in the art as a processor, a mobileprocessor, or a microprocessor, but broadly refers to a microcontroller,a microcomputer, a programmable logic controller, anapplication-specific integrated circuit, and any other programmablecircuit.

In one aspect, interfaces 15 are provided as network interface cards(NICs). Generally, NICs control the sending and receiving of datapackets over a computer network; other types of interfaces 15 may forexample support other peripherals used with computing device 10. Amongthe interfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces,graphics interfaces, and the like. In addition, various types ofinterfaces may be provided such as, for example, universal serial bus(USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radiofrequency (RF), BLUETOOTH™, near-field communications (e.g., usingnear-field magnetics), 802.11 (Wi-Fi), frame relay, TCP/IP, ISDN, fastEthernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) orexternal SATA (ESATA) interfaces, high-definition multimedia interface(HDMI), digital visual interface (DVI), analog or digital audiointerfaces, asynchronous transfer mode (ATM) interfaces, high-speedserial interface (HSSI) interfaces, Point of Sale (POS) interfaces,fiber data distributed interfaces (FDDIs), and the like. Generally, suchinterfaces 15 may include physical ports appropriate for communicationwith appropriate media. In some cases, they may also include anindependent processor (such as a dedicated audio or video processor, asis common in the art for high-fidelity A/V hardware interfaces) and, insome instances, volatile and/or non-volatile memory (e.g., RAM).

Although the system shown in FIG. 7 illustrates one specificarchitecture for a computing device 10 for implementing one or more ofthe aspects described herein, it is by no means the only devicearchitecture on which at least a portion of the features and techniquesdescribed herein may be implemented. For example, architectures havingone or any number of processors 13 may be used, and such processors 13may be present in a single device or distributed among any number ofdevices. In one aspect, a single processor 13 handles communications aswell as routing computations, while in other aspects a separatededicated communications processor may be provided. In various aspects,different types of features or functionalities may be implemented in asystem according to the aspect that includes a client device (such as atablet device or smartphone running client software) and server systems(such as a server system described in more detail below).

Regardless of network device configuration, the system of an aspect mayemploy one or more memories or memory modules (such as, for example,remote memory block 16 and local memory 11) configured to store data,program instructions for the general-purpose network operations, orother information relating to the functionality of the aspects describedherein (or any combinations of the above). Program instructions maycontrol execution of or comprise an operating system and/or one or moreapplications, for example. Memory 16 or memories 11, 16 may also beconfigured to store data structures, configuration data, encryptiondata, historical system operations information, or any other specific orgeneric non-program information described herein.

Because such information and program instructions may be employed toimplement one or more systems or methods described herein, at least somenetwork device aspects may include nontransitory machine-readablestorage media, which, for example, may be configured or designed tostore program instructions, state information, and the like forperforming various operations described herein. Examples of suchnontransitory machine-readable storage media include, but are notlimited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks; magneto-optical mediasuch as optical disks, and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory devices (ROM), flash memory (as is common in mobile devices andintegrated systems), solid state drives (SSD) and “hybrid SSD” storagedrives that may combine physical components of solid state and hard diskdrives in a single hardware device (as are becoming increasingly commonin the art with regard to personal computers), memristor memory, randomaccess memory (RAM), and the like. It should be appreciated that suchstorage means may be integral and non-removable (such as RAM hardwaremodules that may be soldered onto a motherboard or otherwise integratedinto an electronic device), or they may be removable such as swappableflash memory modules (such as “thumb drives” or other removable mediadesigned for rapidly exchanging physical storage devices),“hot-swappable” hard disk drives or solid state drives, removableoptical storage discs, or other such removable media, and that suchintegral and removable storage media may be utilized interchangeably.Examples of program instructions include both object code, such as maybe produced by a compiler, machine code, such as may be produced by anassembler or a linker, byte code, such as may be generated by forexample a JAVA™ compiler and may be executed using a Java virtualmachine or equivalent, or files containing higher level code that may beexecuted by the computer using an interpreter (for example, scriptswritten in Python, Perl, Ruby, Groovy, or any other scripting language).

In some aspects, systems may be implemented on a standalone computingsystem. Referring now to FIG. 8, there is shown a block diagramdepicting a typical exemplary architecture of one or more aspects orcomponents thereof on a standalone computing system. Computing device 20includes processors 21 that may run software that carry out one or morefunctions or applications of aspects, such as for example a clientapplication 24. Processors 21 may carry out computing instructions undercontrol of an operating system 22 such as, for example, a version ofMICROSOFT WINDOWS™ operating system, APPLE macOS™ or iOS™ operatingsystems, some variety of the Linux operating system, ANDROID™ operatingsystem, or the like. In many cases, one or more shared services 23 maybe operable in system 20 and may be useful for providing common servicesto client applications 24. Services 23 may for example be WINDOWS™services, user-space common services in a Linux environment, or anyother type of common service architecture used with operating system 21.Input devices 28 may be of any type suitable for receiving user input,including for example a keyboard, touchscreen, microphone (for example,for voice input), mouse, touchpad, trackball, or any combinationthereof. Output devices 27 may be of any type suitable for providingoutput to one or more users, whether remote or local to system 20, andmay include for example one or more screens for visual output, speakers,printers, or any combination thereof. Memory 25 may be random-accessmemory having any structure and architecture known in the art, for useby processors 21, for example to run software. Storage devices 26 may beany magnetic, optical, mechanical, memristor, or electrical storagedevice for storage of data in digital form (such as those describedabove, referring to FIG. 8). Examples of storage devices 26 includeflash memory, magnetic hard drive, CD-ROM, and/or the like.

In some aspects, systems may be implemented on a distributed computingnetwork, such as one having any number of clients and/or servers.Referring now to FIG. 9, there is shown a block diagram depicting anexemplary architecture 30 for implementing at least a portion of asystem according to one aspect on a distributed computing network.According to the aspect, any number of clients 33 may be provided. Eachclient 33 may run software for implementing client-side portions of asystem; clients may comprise a system 20 such as that illustrated inFIG. 8. In addition, any number of servers 32 may be provided forhandling requests received from one or more clients 33. Clients 33 andservers 32 may communicate with one another via one or more electronicnetworks 31, which may be in various aspects any of the Internet, a widearea network, a mobile telephony network (such as CDMA or GSM cellularnetworks), a wireless network (such as Wi-Fi, WiMAX, LTE, and so forth),or a local area network (or indeed any network topology known in theart; the aspect does not prefer any one network topology over anyother). Networks 31 may be implemented using any known networkprotocols, including for example wired and/or wireless protocols.

In addition, in some aspects, servers 32 may call external services 37when needed to obtain additional information, or to refer to additionaldata concerning a particular call. Communications with external services37 may take place, for example, via one or more networks 31. In variousaspects, external services 37 may comprise web-enabled services orfunctionality related to or installed on the hardware device itself. Forexample, in one aspect where client applications 24 are implemented on asmartphone or other electronic device, client applications 24 may obtaininformation stored in a server system 32 in the cloud or on an externalservice 37 deployed on one or more of a particular enterprise's oruser's premises. In addition to local storage on servers 32, remotestorage 38 may be accessible through the network(s) 31.

In some aspects, clients 33 or servers 32 (or both) may make use of oneor more specialized services or appliances that may be deployed locallyor remotely across one or more networks 31. For example, one or moredatabases 34 in either local or remote storage 38 may be used orreferred to by one or more aspects. It should be understood by onehaving ordinary skill in the art that databases in storage 34 may bearranged in a wide variety of architectures and using a wide variety ofdata access and manipulation means. For example, in various aspects oneor more databases in storage 34 may comprise a relational databasesystem using a structured query language (SQL), while others maycomprise an alternative data storage technology such as those referredto in the art as “NoSQL” (for example, HADOOP CASSANDRA™, GOOGLEBIGTABLE™, and so forth). In some aspects, variant databasearchitectures such as column-oriented databases, in-memory databases,clustered databases, distributed databases, or even flat file datarepositories may be used according to the aspect. It will be appreciatedby one having ordinary skill in the art that any combination of known orfuture database technologies may be used as appropriate, unless aspecific database technology or a specific arrangement of components isspecified for a particular aspect described herein. Moreover, it shouldbe appreciated that the term “database” as used herein may refer to aphysical database machine, a cluster of machines acting as a singledatabase system, or a logical database within an overall databasemanagement system. Unless a specific meaning is specified for a givenuse of the term “database”, it should be construed to mean any of thesesenses of the word, all of which are understood as a plain meaning ofthe term “database” by those having ordinary skill in the art.

Similarly, some aspects may make use of one or more security systems 36and configuration systems 35. Security and configuration management arecommon information technology (IT) and web functions, and some amount ofeach are generally associated with any IT or web systems. It should beunderstood by one having ordinary skill in the art that anyconfiguration or security subsystems known in the art now or in thefuture may be used in conjunction with aspects without limitation,unless a specific security 36 or configuration system 35 or approach isspecifically required by the description of any specific aspect.

FIG. 10 shows an exemplary overview of a computer system 40 as may beused in any of the various locations throughout the system. It isexemplary of any computer that may execute code to process data. Variousmodifications and changes may be made to computer system 40 withoutdeparting from the broader scope of the system and method disclosedherein. Central processor unit (CPU) 41 is connected to bus 42, to whichbus is also connected memory 43, nonvolatile memory 44, display 47,input/output (I/O) unit 48, and network interface card (NIC) 53. I/Ounit 48 may, typically, be connected to peripherals such as a keyboard49, pointing device 50, hard disk 52, real-time clock 51, a camera 57,and other peripheral devices. NIC 53 connects to network 54, which maybe the Internet or a local network, which local network may or may nothave connections to the Internet. The system may be connected to othercomputing devices through the network via a router 55, wireless localarea network 56, or any other network connection. Also shown as part ofsystem 40 is power supply unit 45 connected, in this example, to a mainalternating current (AC) supply 46. Not shown are batteries that couldbe present, and many other devices and modifications that are well knownbut are not applicable to the specific novel functions of the currentsystem and method disclosed herein. It should be appreciated that someor all components illustrated may be combined, such as in variousintegrated applications, for example Qualcomm or Samsungsystem-on-a-chip (SOC) devices, or whenever it may be appropriate tocombine multiple capabilities or functions into a single hardware device(for instance, in mobile devices such as smartphones, video gameconsoles, in-vehicle computer systems such as navigation or multimediasystems in automobiles, or other integrated hardware devices).

In various aspects, functionality for implementing systems or methods ofvarious aspects may be distributed among any number of client and/orserver components. For example, various software modules may beimplemented for performing various functions in connection with a systemof any particular aspect, and such modules may be variously implementedto run on server and/or client components.

According to an aspect, restaurant menu optimization and experimentationmay be performed with a patron who enters a restaurant with a knownpatron profile. The system may predict and offer highly desirable “chefsspecials” that satisfies the patron preferences by making variations ofknown dishes on the restaurant menu. The “chef's special” areautomatically designed by system and may include Artificial Intelligentmethods familiar to those skilled in the art.

According to another aspect, restaurant menu optimization andexperimentation may be performed with a patron who enters a restaurantwith an unknown patron profile. The system may predict and offer highlydesirable “chefs specials” that provide A/B experimentation by makingvariations of known dishes on the restaurant menu and then by tuning themenu to provide an optimal patron menu. The “chef's special” areautomatically designed by system and may include Artificial Intelligentmethods familiar to those skilled in the art.

According to another aspect, recipe optimization may be performed onmultiple patrons at the same time as may be the case for dining partiesof two or more at a restaurant. For example, in a party of four seatedat the same table, of whom three have profile information available tosystem, and one with a raspberry allergy and one is gluten intolerant.The system may predict and offer highly desirable “chefs specials” thatsatisfy each persons preferences amongst those whom food preferences areknown while avoiding allergic ingredients for the whole table. The“chef's special” may include Artificial Intelligent methods familiar tothose skilled in the art.

According to another aspect, patron wearable devices may providereal-time feedback directly into the food design system. For example, aContinuous Glucose Monitor (GCM) may provide input into the recipedevice, and based on patron current glucose level offeradditional/different options for a choice of dessert and/or menu optionsfor future meals.

According to another aspect, a home food inventory system may be used asinput into a recipe generator to provide food preparation options basedon current in home food inventory.

The skilled person will be aware of a range of possible modifications ofthe various aspects described above. Accordingly, the present inventionis defined by the claims and their equivalents.

What is claimed is:
 1. A system for personalized food item design andculinary fulfillment, comprising: a computing device comprising amemory, a processor, and a non-volatile data storage device; a recipedatabase stored on the non-volatile data storage device, the recipedatabase comprising a plurality of recipes, each recipe comprising afood type, a first list of required ingredients and a first requiredculinary skill; a restaurant database stored on the non-volatile datastorage device, the restaurant database comprising a plurality ofrestaurant locations, each restaurant location further comprising: alist of available culinary skills; and a list of available ingredients;a patron profile database stored on the non-volatile data storagedevice, the patron profile database comprising a plurality of patronprofiles, each patron profile comprising: a patron preference; and apatron review for one or more food item recommendations, each food itemrecommendation comprising a second list of required ingredients and asecond required culinary skill; a machine learning algorithm configuredto identify associations among the patron preferences, the first listsof required ingredients, and the first required culinary skills; a fooditem design engine comprising a first plurality of programminginstructions stored in the memory which, when operating on theprocessor, causes the computing device to: convert the patronpreferences, recipes, food items, and patron reviews to a first set ofvector representations; pass the vector representations through themachine learning algorithm to identify associations among the patronpreferences, the first lists of required ingredients, and the firstrequired culinary skills; receive a food item request from a patronportal; convert the food item request to a second set of vectorrepresentations; pass the second set of vector representations throughthe machine learning algorithm to obtain a best fit between the fooditem request and the identified associations, the best fit comprising athird required list of ingredients and a third culinary skill; a patronportal comprising a second plurality of programming instructions storedin the memory which, when operating on the processor, causes thecomputing device to: receive a food item request from a user device, thefood item request comprising a desired food type; retrieve a consumerprofile from a consumer profile database; send the food item request tothe food item design engine; receive the best fit from the food itemdesign engine; generate a food item recommendation from the best fit,choose a restaurant location to prepare the food item recommendation bycomparing the third required list of ingredients and a third culinaryskill against the list of available culinary skills and the list ofavailable ingredients for each restaurant location in the restaurantdatabase; generate culinary instructions comprising a set ofinstructions for preparation of the food item recommendation based onthe third required list of ingredients and a third culinary skill; sendthe food item recommendation to the user device for approval; uponreceipt of an approval of the food item recommendation from the userdevice, send a food item order request to the chosen restaurantlocation, the food item order request comprising the culinaryinstructions.
 2. The system of claim 1, wherein each restaurant locationfurther comprises hours of availability of a chef, and the best fitfurther comprises the hours of availability of the chef of a restaurantlocation.
 3. The system of claim 1, wherein the patron profile isupdated with a patron review of the food item recommendation receivedfrom the user.
 4. The system of claim 1, wherein the patron preferenceis based on social media information retrieved from a social medianetwork.
 5. The system of claim 1, wherein the patron preference isbased on nutritional data retrieved from a third-party resource over anetwork.
 6. The system of claim 5, wherein the nutritional datacomprises allergy information.
 7. A method for personalized food itemdesign and culinary fulfillment, comprising the steps of: storing arecipe database on a non-volatile data storage device of a computingdevice comprising a memory, a processor, and the non-volatile datastorage device, the recipe database comprising a plurality of recipes,each recipe comprising a food type, a first list of required ingredientsand a first required culinary skill; storing a restaurant database onthe non-volatile data storage device, the restaurant database comprisinga plurality of restaurant locations, each restaurant location furthercomprising: a list of available culinary skills; and a list of availableingredients; storing a patron profile database stored on thenon-volatile data storage device, the patron profile database comprisinga plurality of patron profiles, each patron profile comprising: a patronpreference; and a patron review for one or more food itemrecommendations, each food item recommendation comprising a second listof required ingredients and a second required culinary skill;configuring a machine learning algorithm to identify associations amongthe patron preferences, the first lists of required ingredients, and thefirst required culinary skills; using a food item design engineoperating on the computing device to: convert the patron preferences,recipes, food items, and patron reviews to a first set of vectorrepresentations; pass the vector representations through the machinelearning algorithm to identify associations among the patronpreferences, the first lists of required ingredients, and the firstrequired culinary skills; receive a food item request from a patronportal; convert the food item request to a second set of vectorrepresentations; and pass the second set of vector representationsthrough the machine learning algorithm to obtain a best fit between thefood item request and the identified associations, the best fitcomprising a third required list of ingredients and a third culinaryskill; and using a patron portal operating on the computing device to:receive a food item request from a user device, the food item requestcomprising a desired food type; retrieve a consumer profile from aconsumer profile database; send the food item request to the food itemdesign engine; receive the best fit from the food item design engine;generate a food item recommendation from the best fit; choose arestaurant location to prepare the food item recommendation by comparingthe third required list of ingredients and a third culinary skillagainst the list of available culinary skills and the list of availableingredients for each restaurant location in the restaurant database;generate culinary instructions comprising a set of instructions forpreparation of the food item recommendation based on the third requiredlist of ingredients and a third culinary skill; send the food itemrecommendation to the user device for approval; and upon receipt of anapproval of the food item recommendation from the user device, send afood item order request to the chosen restaurant location, the food itemorder request comprising the culinary instructions.
 8. The method ofclaim 7, wherein each restaurant location further comprises hours ofavailability of a chef, and the best fit further comprises the hours ofavailability of the chef of a restaurant location.
 9. The method ofclaim 7, wherein the patron profile is updated with a patron review ofthe food item recommendation received from the user.
 10. The method ofclaim 7, wherein the patron preference is based on social mediainformation retrieved from a social media network.
 11. The method ofclaim 7, wherein the patron preference is based on nutritional dataretrieved from a third-party resource over a network.
 12. The method ofclaim 11, wherein the nutritional data comprises allergy information.